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2020 – today
- 2024
[j4]Vito Bellini, Eugenio Di Sciascio, Francesco Maria Donini
, Claudio Pomo, Azzurra Ragone, Angelo Schiavone:
A qualitative analysis of knowledge graphs in recommendation scenarios through semantics-aware autoencoders. J. Intell. Inf. Syst. 62(3): 787-807 (2024)- [c34]Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen:
EB-NeRD a large-scale dataset for news recommendation. RecSys Challenge 2024: 1-11 - [c33]Antonio Ferrara, Marco Valentini, Paolo Masciullo, Antonio De Candia, Davide Abbattista, Riccardo Fusco, Claudio Pomo, Vito Walter Anelli, Giovanni Maria Biancofiore, Ludovico Boratto, Fedelucio Narducci:
DIVAN: Deep-Interest Virality-Aware Network to Exploit Temporal Dynamics in News Recommendation. RecSys Challenge 2024: 12-16 - [c32]Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Tommaso Di Noia, Eugenio Di Sciascio:
A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph. RecSys 2024: 549-559 - [c31]Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen:
RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations. RecSys 2024: 1195-1199 - [c30]Matteo Attimonelli, Danilo Danese, Daniele Malitesta, Claudio Pomo, Giuseppe Gassi, Tommaso Di Noia:
Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation. WWW (Companion Volume) 2024: 1075-1078 - [i20]Matteo Attimonelli, Danilo Danese, Daniele Malitesta, Claudio Pomo, Giuseppe Gassi, Tommaso Di Noia:
Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation. CoRR abs/2403.04503 (2024) - [i19]Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Fragkiskos D. Malliaros, Tommaso Di Noia:
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach. CoRR abs/2403.19841 (2024) - [i18]Matteo Attimonelli, Claudio Pomo, Dietmar Jannach, Tommaso Di Noia:
Fashion Image-to-Image Translation for Complementary Item Retrieval. CoRR abs/2408.09847 (2024) - [i17]Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Tommaso Di Noia, Eugenio Di Sciascio:
A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph. CoRR abs/2408.11762 (2024) - [i16]Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Tommaso Di Noia, Fragkiskos D. Malliaros:
Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation? CoRR abs/2408.11767 (2024) - [i15]Matteo Attimonelli, Danilo Danese, Angela Di Fazio, Daniele Malitesta, Claudio Pomo, Tommaso Di Noia:
Ducho meets Elliot: Large-scale Benchmarks for Multimodal Recommendation. CoRR abs/2409.15857 (2024) - [i14]Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen:
RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations. CoRR abs/2409.20483 (2024) - 2023
- [j3]Giandomenico Cornacchia, Vito Walter Anelli, Giovanni Maria Biancofiore, Fedelucio Narducci, Claudio Pomo, Azzurra Ragone, Eugenio Di Sciascio:
Auditing fairness under unawareness through counterfactual reasoning. Inf. Process. Manag. 60(2): 103224 (2023) - [c29]Simona Colucci, Tommaso Di Noia, Francesco M. Donini, Claudio Pomo, Eugenio Di Sciascio:
Irrelevant Explanations: a Logical Formalization and a Case Study. XAI.it@AI*IA 2023: 67-75 - [c28]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Vincenzo Paparella, Claudio Pomo:
Auditing Consumer- and Producer-Fairness in Graph Collaborative Filtering. ECIR (1) 2023: 33-48 - [c27]Dario Di Palma, Vito Walter Anelli, Daniele Malitesta, Vincenzo Paparella, Claudio Pomo, Yashar Deldjoo, Tommaso Di Noia:
Examining Fairness in Graph-Based Collaborative Filtering: A Consumer and Producer Perspective. IIR 2023: 79-84 - [c26]Vincenzo Paparella, Alberto Carlo Maria Mancino, Antonio Ferrara, Claudio Pomo, Vito Walter Anelli, Tommaso Di Noia:
Knowledge Graph Datasets for Recommendation. KaRS@RecSys 2023: 109-117 - [c25]Federico Bianchi, Patrick John Chia, Jacopo Tagliabue, Ciro Greco, Gabriel de Souza P. Moreira, Davide Eynard, Fahd Husain, Claudio Pomo:
EvalRS 2023: Well-Rounded Recommender Systems for Real-World Deployments. KDD 2023: 5851-5852 - [c24]Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di Noia:
Disentangling the Performance Puzzle of Multimodal-aware Recommender Systems. EvalRS@KDD 2023 - [c23]Daniele Malitesta, Giuseppe Gassi, Claudio Pomo, Tommaso Di Noia:
Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation. ACM Multimedia 2023: 9668-9671 - [c22]Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di Noia:
On Popularity Bias of Multimodal-aware Recommender Systems: A Modalities-driven Analysis. MMIR@MM 2023: 59-68 - [c21]Vito Walter Anelli, Daniele Malitesta, Claudio Pomo, Alejandro Bellogín, Eugenio Di Sciascio, Tommaso Di Noia:
Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis. RecSys 2023: 350-361 - [c20]Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Tommaso Di Noia, Antonio Ferrara:
An Out-of-the-Box Application for Reproducible Graph Collaborative Filtering extending the Elliot Framework. UMAP (Adjunct Publication) 2023: 12-15 - [e1]Federico Bianchi, Patrick John Chia, Ciro Greco, Claudio Pomo, Gabriel de Souza P. Moreira, Davide Eynard, Fahd Husain, Jacopo Tagliabue:
Proceedings of EvalRS: A Rounded Evaluation Of Recommender Systems 2023 co-located with 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD 2023), Long Beach,CA, USA, August 7, 2023. CEUR Workshop Proceedings 3450, CEUR-WS.org 2023 [contents] - [i13]Federico Bianchi, Patrick John Chia, Ciro Greco, Claudio Pomo, Gabriel de Souza P. Moreira, Davide Eynard, Fahd Husain, Jacopo Tagliabue:
EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments. CoRR abs/2304.07145 (2023) - [i12]Daniele Malitesta, Giuseppe Gassi, Claudio Pomo, Tommaso Di Noia:
Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation. CoRR abs/2306.17125 (2023) - [i11]Vito Walter Anelli, Daniele Malitesta, Claudio Pomo, Alejandro Bellogín, Tommaso Di Noia, Eugenio Di Sciascio:
Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis. CoRR abs/2308.00404 (2023) - [i10]Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Eugenio Di Sciascio, Tommaso Di Noia:
A Topology-aware Analysis of Graph Collaborative Filtering. CoRR abs/2308.10778 (2023) - [i9]Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di Noia:
On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis. CoRR abs/2308.12911 (2023) - [i8]Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Felice Antonio Merra, Tommaso Di Noia, Eugenio Di Sciascio:
Formalizing Multimedia Recommendation through Multimodal Deep Learning. CoRR abs/2309.05273 (2023) - [i7]Daniele Malitesta, Claudio Pomo, Tommaso Di Noia:
Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node Representation. CoRR abs/2310.11270 (2023) - 2022
- [j2]Tommaso Di Noia, Francesco Maria Donini, Dietmar Jannach, Fedelucio Narducci, Claudio Pomo:
Conversational recommendation: Theoretical model and complexity analysis. Inf. Sci. 614: 325-347 (2022) - [c19]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Antonio Ferrara, Daniele Malitesta, Claudio Pomo:
Reshaping Graph Recommendation with Edge Graph Collaborative Filtering and Customer Reviews. DL4SR@CIKM 2022 - [c18]Tommaso Di Noia, Francesco Maria Donini, Dietmar Jannach, Fedelucio Narducci, Claudio Pomo:
Towards a theoretical formalization of conversational recommendation. CIKM Workshops 2022 - [c17]Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Francesco Maria Donini, Vincenzo Paparella, Claudio Pomo:
An Analysis of Local Explanation with LIME-RS. IIR 2022 - [c16]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Antonio Ferrara, Daniele Malitesta, Claudio Pomo:
How Neighborhood Exploration influences Novelty and Diversity in Graph Collaborative Filtering. MORS@RecSys 2022 - [c15]Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco M. Donini, Eugenio Di Sciascio, Tommaso Di Noia:
The Challenging Reproducibility Task in Recommender Systems Research between Traditional and Deep Learning Models. SEBD 2022: 514-521 - [c14]Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Dietmar Jannach, Claudio Pomo:
Top-N Recommendation Algorithms: A Quest for the State-of-the-Art. UMAP 2022: 121-131 - [i6]Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Dietmar Jannach, Claudio Pomo:
Top-N Recommendation Algorithms: A Quest for the State-of-the-Art. CoRR abs/2203.01155 (2022) - 2021
- [c13]Giandomenico Cornacchia, Francesco M. Donini, Fedelucio Narducci, Claudio Pomo, Azzurra Ragone:
Explanation in Multi-Stakeholder Recommendation for Enterprise Decision Support Systems. CAiSE Workshops 2021: 39-47 - [c12]Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, Eugenio Di Sciascio, Tommaso Di Noia:
How to Perform Reproducible Experiments in the ELLIOT Recommendation Framework: Data Processing, Model Selection, and Performance Evaluation. IIR 2021 - [c11]Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, Tommaso Di Noia:
V-Elliot: Design, Evaluate and Tune Visual Recommender Systems. RecSys 2021: 768-771 - [c10]Vito Walter Anelli, Luca Belli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara, Fedelucio Narducci, Claudio Pomo:
Pursuing Privacy in Recommender Systems: the View of Users and Researchers from Regulations to Applications. RecSys 2021: 838-841 - [c9]Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Francesco Maria Donini, Vincenzo Paparella, Claudio Pomo:
Adherence and Constancy in LIME-RS Explanations for Recommendation (Long paper). KaRS/ComplexRec@RecSys 2021 - [c8]Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Claudio Pomo:
Reenvisioning the comparison between Neural Collaborative Filtering and Matrix Factorization. RecSys 2021: 521-529 - [c7]Juri Di Rocco, Davide Di Ruscio, Claudio Di Sipio, Phuong Thanh Nguyen, Claudio Pomo:
On the Need for a Body of Knowledge on Recommender Systems (Short paper). KaRS/ComplexRec@RecSys 2021 - [c6]Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, Tommaso Di Noia:
Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation. SIGIR 2021: 2405-2414 - [i5]Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco M. Donini, Tommaso Di Noia:
Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation. CoRR abs/2103.02590 (2021) - [i4]Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Claudio Pomo:
Reenvisioning Collaborative Filtering vs Matrix Factorization. CoRR abs/2107.13472 (2021) - [i3]Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Francesco Maria Donini, Vincenzo Paparella, Claudio Pomo:
Adherence and Constancy in LIME-RS Explanations for Recommendation. CoRR abs/2109.00818 (2021) - [i2]Tommaso Di Noia, Francesco M. Donini, Dietmar Jannach, Fedelucio Narducci, Claudio Pomo:
Conversational Recommendation: Theoretical Model and Complexity Analysis. CoRR abs/2111.05578 (2021) - 2020
- [c5]Vito Bellini, Giovanni Maria Biancofiore, Tommaso Di Noia, Eugenio Di Sciascio, Fedelucio Narducci, Claudio Pomo:
GUapp: A Conversational Agent for Job Recommendation for the Italian Public Administration. EAIS 2020: 1-7 - [c4]Carmelo Ardito, Tommaso Di Noia, Eugenio Di Sciascio, Domenico Lofù, Giulio Mallardi, Claudio Pomo, Felice Vitulano:
Towards a Trustworthy Patient Home-Care Thanks to an Edge-Node Infrastructure. HCSE 2020: 181-189
2010 – 2019
- 2019
- [c3]Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone, Claudio Pomo:
Semantic interpretability of latent factors for recommendation. IIR 2019: 43-44 - [c2]Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Claudio Pomo, Azzurra Ragone:
On the discriminative power of hyper-parameters in cross-validation and how to choose them. RecSys 2019: 447-451 - [i1]Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Claudio Pomo, Azzurra Ragone:
On the discriminative power of Hyper-parameters in Cross-Validation and how to choose them. CoRR abs/1909.02523 (2019) - 2018
- [j1]Mariano Pulpito, Paolo Fornarelli, Claudio Pomo, Pietro Boccadoro, Luigi Alfredo Grieco:
On fast prototyping LoRaWAN: a cheap and open platform for daily experiments. IET Wirel. Sens. Syst. 8(5): 237-245 (2018) - 2017
- [c1]Francesco Bruni, Claudio Pomo, Gaetano Murgolo:
Four Key Factors to Design a Web of Things Architecture. ICWE Workshops 2017: 87-91
Coauthor Index
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last updated on 2024-10-25 21:16 CEST by the dblp team
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